scRNAseq Interstitial cells : What happens over time?
Marion Hardy
2024-03-04
Introduction
Data from our collaborator Panagiotis rds file for a SingleCellExperiment object containing the single cell data for the interstitial cells of Hydra Vulgaris during multiples stages of regeneration after bisection:
BUT they mapped it to Hydra Magnipapillata (102 version of 105) “Drop-seq reads from 15 libraries generated for Hydra vulgaris strain AEP?? were mapped to the 2.0 genome assembly of closely related Hydra vulgaris strain 102 (available at https://research.nhgri.nih.gov/hydra/) and processed using the Hydra 2.0 gene models. Strain Hydra vulgaris 102 was formerly referred to as Hydra magnipapillata.”
The coldata of the object contain cell annotation including
quality metrics: nFeature nCount (not MT percentage interestingly)
batch info: either 2869 (3162 barcodes), 3113 (10352 barcodes), 3271 (13279 barcodes), 3357 (3875 barcodes)
originating experiments (head or foot regeneration)
experimental time points
pseudo-axis assignment (vals.axis ranging from 0-1, increasing in the foot-tentacle direction)
mitotic and apoptotic signatures indices from 0 to 1
The rowdata contains gene annotation, using Entrez-gene identifiers. I have also noticed that in the sce objects there’s
PCA, tSNE and UMAP coordinates for reduced dimensions + corrected for batch values
assay metafeatures hold gene_id, product, gene, is.rib.prot.gene (T/F), HypoMarkers (T/F), ccyle (T/F), apopt (T/F) etc
I converted the sce objects 6.6gb into a seurat object 2.2 gb and checked that all cited parameters could be found in it.
Summary of 2/28 meeting with Hannah and Celina
- REG_FOOT = It’s a head that is regenerating a foot (counterintuitive)
- Clusters have been attributed with confidence except 10 which is likely doublets but should be explored further
- Cluster 5 are probably glands but I haven’t found a characterized marker I could use with the Hydra atlas, for mow they’re called ZymoG/unknown
- Update the annotations by crossreferencing genes ID from 105, getting the sequence and blasting to find the 102 LOC ID.-> Done
- Do population number evolution over time (not just neurons!) -> Done
- Cluster with 0.1 resolution -> Done
- Aligning to the 105 genome -> Slurm script being written, Hannah downloading GEO dataset
Additional meeting with Hannah summary:
Because the authors only say they sequenced 20-40 hydras per timepoint, cell counts should be relative to total amount of cells
Plot these transcription factors of interest over time (DotPlot + split Featureplot)
Data loading and upate on the project
In this report we’re gonna plot transcription factors of interest over the different timepoints. I also wanted to add cell population counts over time. Maybe I could learn to plot gene expression over time too? Anyway, we’re exploring gene expression programs and cell populations over time.
Changing UMAP resolution
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 29280
## Number of edges: 889358
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9832
## Number of communities: 21
## Elapsed time: 2 seconds
target <- c("NSP4","midasin","mini-COL8","SP-D-like","FH20-X3","CAII",
"CELA3B","zinc-carboxypeptidase","ANO39","TYN1","H2A.2.2","nas2-X2",
"Lwamide-X1","DMRT1","TUBA4A-X1","HTRA3","polRF-X1","ec3A","ec3B",
"nop58-X1","OTP","MEP1A","rhammosyl-O-methyltransferase","hywnt3",
"PPOD1","ks1","hyAlx","ELAV2","POU4","MUC2", "ec3", "grm1","myc",
"myc1","wnt3","ec2","ec1B","ec4","ec1A","en1","en1_NDF1_DANRE",
"ec5","en1","en2A","en2B") 
Subsetting by head/foot and timepoint
Cell population evolution over time
Regenerating a head
“Raw” cell numbers
| REG_HEAD_t0 | REG_HEAD_t06 | REG_HEAD_t12 | REG_HEAD_t24 | REG_HEAD_t48 | REG_HEAD_t96 | |
|---|---|---|---|---|---|---|
| doublets/triplets | 36 | 27 | 29 | 18 | 24 | 56 |
| earlyGc | 684 | 574 | 129 | 208 | 278 | 253 |
| earlyNem | 202 | 209 | 148 | 84 | 128 | 78 |
| earlyNeur | 59 | 65 | 125 | 96 | 102 | 78 |
| ec1A | 67 | 55 | 48 | 64 | 50 | 112 |
| ec1A/ec1B | 0 | 0 | 0 | 0 | 3 | 58 |
| ec2 | 0 | 0 | 0 | 0 | 1 | 56 |
| ec3n/en1n | 76 | 64 | 71 | 80 | 65 | 168 |
| ec4 | 0 | 0 | 0 | 2 | 6 | 71 |
| GranG/ZymoG | 256 | 246 | 204 | 212 | 223 | 320 |
| ISC | 488 | 388 | 420 | 201 | 339 | 384 |
| Nb | 1549 | 1398 | 870 | 553 | 737 | 1288 |
| unknown/ZymoG? | 64 | 73 | 66 | 60 | 95 | 115 |
| Sum | 3481 | 3099 | 2110 | 1578 | 2051 | 3037 |
Relative number of cells
| REG_HEAD_t0 | REG_HEAD_t06 | REG_HEAD_t12 | REG_HEAD_t24 | REG_HEAD_t48 | REG_HEAD_t96 | |
|---|---|---|---|---|---|---|
| doublets/triplets | 1.03 | 0.87 | 1.37 | 1.14 | 1.17 | 1.84 |
| earlyGc | 19.65 | 18.52 | 6.11 | 13.18 | 13.55 | 8.33 |
| earlyNem | 5.80 | 6.74 | 7.01 | 5.32 | 6.24 | 2.57 |
| earlyNeur | 1.69 | 2.10 | 5.92 | 6.08 | 4.97 | 2.57 |
| ec1A | 1.92 | 1.77 | 2.27 | 4.06 | 2.44 | 3.69 |
| ec1A/ec1B | 0.00 | 0.00 | 0.00 | 0.00 | 0.15 | 1.91 |
| ec2 | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 1.84 |
| ec3n/en1n | 2.18 | 2.07 | 3.36 | 5.07 | 3.17 | 5.53 |
| ec4 | 0.00 | 0.00 | 0.00 | 0.13 | 0.29 | 2.34 |
| GranG/ZymoG | 7.35 | 7.94 | 9.67 | 13.43 | 10.87 | 10.54 |
| ISC | 14.02 | 12.52 | 19.91 | 12.74 | 16.53 | 12.64 |
| Nb | 44.50 | 45.11 | 41.23 | 35.04 | 35.93 | 42.41 |
| unknown/ZymoG? | 1.84 | 2.36 | 3.13 | 3.80 | 4.63 | 3.79 |
| Sum | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
Notice how early Gc decrease almost 5x between 12-24 hours while ISC, GranG and early neurons spike up 2 to 3X. I think that population is called upon to create not only gland cells but can help support other progenitors.
Regenerating a foot
“Raw” cell numbers
| REG_FOOT_t0 | REG_FOOT_t06 | REG_FOOT_t12 | REG_FOOT_t24 | REG_FOOT_t48 | REG_FOOT_t96 | |
|---|---|---|---|---|---|---|
| doublets/triplets | 38 | 31 | 28 | 22 | 27 | 19 |
| earlyGc | 60 | 41 | 38 | 51 | 204 | 85 |
| earlyNem | 200 | 266 | 211 | 236 | 86 | 205 |
| earlyNeur | 199 | 201 | 176 | 143 | 66 | 100 |
| ec1A | 51 | 57 | 42 | 54 | 36 | 55 |
| ec1A/ec1B | 59 | 77 | 58 | 51 | 64 | 55 |
| ec2 | 80 | 103 | 77 | 63 | 84 | 51 |
| ec3n/en1n | 91 | 87 | 94 | 75 | 56 | 68 |
| ec4 | 49 | 48 | 46 | 43 | 43 | 32 |
| GranG/ZymoG | 332 | 374 | 280 | 269 | 189 | 196 |
| ISC | 581 | 452 | 401 | 390 | 320 | 287 |
| Nb | 948 | 790 | 731 | 797 | 559 | 713 |
| unknown/ZymoG? | 149 | 152 | 143 | 131 | 65 | 93 |
| Sum | 2837 | 2679 | 2325 | 2325 | 1799 | 1959 |
Relative number of cells
| REG_FOOT_t0 | REG_FOOT_t06 | REG_FOOT_t12 | REG_FOOT_t24 | REG_FOOT_t48 | REG_FOOT_t96 | |
|---|---|---|---|---|---|---|
| doublets/triplets | 1.34 | 1.16 | 1.20 | 0.95 | 1.50 | 0.97 |
| earlyGc | 2.11 | 1.53 | 1.63 | 2.19 | 11.34 | 4.34 |
| earlyNem | 7.05 | 9.93 | 9.08 | 10.15 | 4.78 | 10.46 |
| earlyNeur | 7.01 | 7.50 | 7.57 | 6.15 | 3.67 | 5.10 |
| ec1A | 1.80 | 2.13 | 1.81 | 2.32 | 2.00 | 2.81 |
| ec1A/ec1B | 2.08 | 2.87 | 2.49 | 2.19 | 3.56 | 2.81 |
| ec2 | 2.82 | 3.84 | 3.31 | 2.71 | 4.67 | 2.60 |
| ec3n/en1n | 3.21 | 3.25 | 4.04 | 3.23 | 3.11 | 3.47 |
| ec4 | 1.73 | 1.79 | 1.98 | 1.85 | 2.39 | 1.63 |
| GranG/ZymoG | 11.70 | 13.96 | 12.04 | 11.57 | 10.51 | 10.01 |
| ISC | 20.48 | 16.87 | 17.25 | 16.77 | 17.79 | 14.65 |
| Nb | 33.42 | 29.49 | 31.44 | 34.28 | 31.07 | 36.40 |
| unknown/ZymoG? | 5.25 | 5.67 | 6.15 | 5.63 | 3.61 | 4.75 |
| Sum | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
Notice how early neurons only go up after a spike in earlGc. Is the opposite happening here?
Transcription factors
We are looking at transcription factors of interest that, based on bulk RNAseq, appeared active only in the injured animal, not the homeostatic one.
Dotplot
Feature plot
Regenrating a head
It looks like the appearing cells might be the one with the transcription factor activity
Regenerating a foot
Session info
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.5.0 scCustomize_1.1.1 Seurat_5.0.1 SeuratObject_5.0.0
## [5] sp_1.5-1 openxlsx_4.2.5.1 forcats_0.5.2 stringr_1.4.1
## [9] dplyr_1.0.10 purrr_0.3.5 readr_2.1.3 tidyr_1.2.1
## [13] tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 spatstat.explore_3.0-5 reticulate_1.26
## [4] tidyselect_1.2.0 htmlwidgets_1.5.4 grid_4.2.2
## [7] Rtsne_0.16 munsell_0.5.0 codetools_0.2-18
## [10] ica_1.0-3 future_1.29.0 miniUI_0.1.1.1
## [13] withr_2.5.0 spatstat.random_3.0-1 colorspace_2.0-3
## [16] progressr_0.11.0 highr_0.9 knitr_1.41
## [19] rstudioapi_0.14 stats4_4.2.2 ROCR_1.0-11
## [22] ggsignif_0.6.4 tensor_1.5 listenv_0.8.0
## [25] labeling_0.4.2 polyclip_1.10-4 farver_2.1.1
## [28] parallelly_1.32.1 vctrs_0.5.0 generics_0.1.3
## [31] xfun_0.34 timechange_0.1.1 R6_2.5.1
## [34] doParallel_1.0.17 clue_0.3-62 ggbeeswarm_0.7.2
## [37] spatstat.utils_3.0-1 cachem_1.0.6 assertthat_0.2.1
## [40] promises_1.2.0.1 scales_1.2.1 googlesheets4_1.0.1
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## [46] globals_0.16.2 goftest_1.2-3 spam_2.10-0
## [49] rlang_1.0.6 GlobalOptions_0.1.2 splines_4.2.2
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## [160] rematch2_2.1.2 RcppHNSW_0.5.0 irlba_2.3.5.1
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